An analysis of patient safety incident reports describing injuries to staff working in critical care in the North West of England between 2009 and 2013

SummaryBackground: With the widespread use of electronic health records (EHRs) for many clinical tasks, interoperability with other health information technology (health IT) is critical for the effective delivery of care. While it is generally recognized that poor interoperability negatively impacts patient care, little is known about the specific patient safety implications. Understanding the patient safety implications will help prioritize interoperability efforts around architectures and standards.Objectives: Our objectives were to (1) identify patient safety incident reports that reflect EHR interoperability challenges with other health IT, and (2) perform a detailed analysis of these reports to understand the health IT systems involved, the clinical care processes impacted, whether the incident occurred within or between provider organizations, and the reported severity of the patient safety events.Methods: From a database of 1.735 million patient safety event (PSE) reports spanning multiple provider organizations, 2625 reports that were indicated as being health IT related by the event reporter were reviewed to identify EHR interoperability related reports. Through a rigorous coding process 209 EHR interoperability related events were identified and coded.Results: The majority of EHR interoperability PSE reports involved interfacing with pharmacy systems (i.e. medication related), followed by laboratory, and radiology. Most of the interoperability challenges in these clinical areas were associated with the EHR receiving information from other health IT systems as opposed to the EHR sending information to other systems. The majority of EHR interoperability challenges were within a provider organization and while many of the safety events reached the patient, only a few resulted in patient harm.Conclusions: Interoperability efforts should prioritize systems in pharmacy, laboratory, and radiology. Providers should recognize the need to improve EHRs interfacing with other health IT systems within their own organization.Citation: Adams KT, Howe JL, Fong A, Puthumana JS, Kellogg KM, Gaunt M, Ratwani RM. An analysis of patient safety incident reports associated with electronic health record interoperability. Appl Clin Inform 2017; 8: 593–602 https://doi.org/10.4338/ACI-2017-01-RA-0014

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Analysis of Patient Safety Incident reporting system as an indicator of quality nursing in critical care units in KwaZulu-Natal, South Africa

Health SA Gesondheid ◽

10.4102/hsag.v25i0.1263 ◽

2020 ◽

Vol 25 ◽

Author(s):

Thusile M. Gqaleni ◽

Busisiwe R. Bhengu

Keyword(s):

South Africa ◽

Critical Care ◽

Patient Safety ◽

Reporting System ◽

Critical Care Units ◽

Incident Reporting System ◽

Safety Incident ◽

Kwazulu Natal ◽

Patient Safety Incident ◽

Safety Incident Reporting

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Using multiclass classification to automate the identification of patient safety incident reports by type and severity

BMC Medical Informatics and Decision Making ◽

10.1186/s12911-017-0483-8 ◽

2017 ◽

Vol 17 (1) ◽

Cited By ~ 19

Author(s):

Ying Wang ◽

Enrico Coiera ◽

William Runciman ◽

Farah Magrabi

Keyword(s):

Patient Safety ◽

Multiclass Classification ◽

Safety Incident ◽

Patient Safety Incident ◽

Incident Reports

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Using convolutional neural networks to identify patient safety incident reports by type and severity

Journal of the American Medical Informatics Association ◽

10.1093/jamia/ocz146 ◽

2019 ◽

Vol 26 (12) ◽

pp. 1600-1608 ◽

Cited By ~ 3

Author(s):

Ying Wang ◽

Enrico Coiera ◽

Farah Magrabi

Keyword(s):

Patient Safety ◽

Test Data ◽

Reporting System ◽

Word Embedding ◽

Support Vector ◽

Data Sets ◽

Safety Incident ◽

Patient Safety Incident ◽

Incident Reports ◽

Severity Levels

Abstract Objective To evaluate the feasibility of a convolutional neural network (CNN) with word embedding to identify the type and severity of patient safety incident reports. Materials and Methods A CNN with word embedding was applied to identify 10 incident types and 4 severity levels. Model training and validation used data sets (n_type = 2860, n_severity = 1160) collected from a statewide incident reporting system. Generalizability was evaluated using an independent hospital-level reporting system. CNN architectures were examined by varying layer size and hyperparameters. Performance was evaluated by F score, precision, recall, and compared to binary support vector machine (SVM) ensembles on 3 testing data sets (type/severity: n_benchmark = 286/116, n_original = 444/4837, n_independent = 6000/5950). Results A CNN with 6 layers was the most effective architecture, outperforming SVMs with better generalizability to identify incidents by type and severity. The CNN achieved high F scores (> 85%) across all test data sets when identifying common incident types including falls, medications, pressure injury, and aggression. When identifying common severity levels (medium/low), CNN outperformed SVMs, improving F scores by 11.9%–45.1% across all 3 test data sets. Discussion Automated identification of incident reports using machine learning is challenging because of a lack of large labelled training data sets and the unbalanced distribution of incident classes. The standard classification strategy is to build multiple binary classifiers and pool their predictions. CNNs can extract hierarchical features and assist in addressing class imbalance, which may explain their success in identifying incident report types. Conclusion A CNN with word embedding was effective in identifying incidents by type and severity, providing better generalizability than SVMs.

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